United States Earnings Dynamics: Inequality, Mobility, and Volatility
September 2020
Working Paper Number:
CES-20-29
Abstract
Using data from the Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) infrastructure files, we study changes over time and across sub-national populations in the distribution of real labor earnings. We consider four large MSAs (Detroit, Los Angeles, New York, and San Francisco) for the period 1998 to 2017, with particular attention paid to the subperiods before, during, and after the Great Recession. For the four large MSAs we analyze, there are clear national trends represented in each of the local areas, the most prominent of which is the increase in the share of earnings accruing to workers at the top of the earnings distribution in 2017 compared with 1998. However, the magnitude of these trends varies across MSAs, with New York and San Francisco showing relatively large increases and Los Angeles somewhere in the middle relative to Detroit whose total real earnings distribution is relatively stable over the period. Our results contribute to the emerging literature on differences between national and regional economic outcomes, exemplifying what will be possible with a new data exploration tool'the Earnings and Mobility Statistics (EAMS) web application'currently under development at the U.S. Census Bureau.
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macroeconomic,
data census,
quarterly,
aggregate,
earnings,
employed,
sector,
yearly,
recession,
regional,
workforce,
salary,
gdp,
employment wages,
fiscal,
workers earnings,
employment statistics,
mobility,
employment earnings,
workforce indicators,
employment trends,
employment distribution
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Social Security Administration,
National Science Foundation,
Current Population Survey,
Social Security,
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American Community Survey,
Social Security Number,
National Institute on Aging,
Alfred P Sloan Foundation,
Longitudinal Employer Household Dynamics,
Quarterly Workforce Indicators,
Census Bureau Disclosure Review Board,
Disclosure Review Board,
International Trade Research Report
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